scholarly journals Real-time bidding campaigns optimization using user profile settings

2021 ◽  
Author(s):  
Luis Miralles-Pechuán ◽  
M. Atif Qureshi ◽  
Brian Mac Namee
Keyword(s):  
Real Time ◽  
Online Advertising ◽  
User Profile ◽  
Search Space ◽  
Bidding Strategy ◽  
Best Value ◽  
Number Of Visits ◽  
Reduced Space ◽  
And Gender ◽  
Multiple Configurations

AbstractReal-Time bidding is nowadays one of the most promising systems in the online advertising ecosystem. In the presented study, the performance of RTB campaigns is improved by optimising the parameters of the users’ profiles and the publishers’ websites. Most studies about optimising RTB campaigns are focused on the bidding strategy; estimating the best value for each bid. However, our research is focused on optimising RTB campaigns by finding out configurations that maximise both the number of impressions and the average profitability of the visits. An online campaign configuration generally consists of a set of parameters along with their values such as {Browser = “Chrome”, Country = “Germany”, Age = “20–40” and Gender = “Woman”}. The experiments show that, when the number of required visits by advertisers is low, it is easy to find configurations with high average profitability, but as the required number of visits increases, the average profitability diminishes. Additionally, configuration optimisation has been combined with other interesting strategies to increase, even more, the campaigns’ profitability. In particular, the presented study considers the following complementary strategies to increase profitability: (1) selecting multiple configurations with a small number of visits rather than a unique configuration with a large number of visits, (2) discarding visits according to certain cost and profitability thresholds, (3) analysing a reduced space of the dataset and extrapolating the solution over the whole dataset, and (4) increasing the search space by including solutions below the required number of visits. The developed campaign optimisation methodology could be offered by RTB and other advertising platforms to advertisers to make their campaigns more profitable.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

scholarly journals Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Sen Zhang ◽  
Qiang Fu ◽  
Wendong Xiao
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Historical Data ◽  
User Profile ◽  
Imbalanced Learning ◽  
Sample Distribution ◽  
The Real ◽  
Adaboost Algorithm ◽  
Click Through Rate ◽  
Prediction Approach

Accurate click-through rate (CTR) prediction can not only improve the advertisement company’s reputation and revenue, but also help the advertisers to optimize the advertising performance. There are two main unsolved problems of the CTR prediction: low prediction accuracy due to the imbalanced distribution of the advertising data and the lack of the real-time advertisement bidding implementation. In this paper, we will develop a novel online CTR prediction approach by incorporating the real-time bidding (RTB) advertising by the following strategies: user profile system is constructed from the historical data of the RTB advertising to describe the user features, the historical CTR features, the ID features, and the other numerical features. A novel CTR prediction approach is presented to address the imbalanced learning sample distribution by integrating the Weighted-ELM (WELM) and the Adaboost algorithm. Compared to the commonly used algorithms, the proposed approach can improve the CTR significantly.

scholarly journals Soft Biometrics for a Socially Assistive Robotic Platform

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Pierluigi Carcagnì ◽  
Dario Cazzato ◽  
Marco Del Coco ◽  
Pier Luigi Mazzeo ◽  
Marco Leo ◽  
...  
Keyword(s):  
Real Time ◽  
Humanoid Robot ◽  
Field Of View ◽  
Robotic Platform ◽  
Time System ◽  
Real Time System ◽  
Age And Gender ◽  
Gender And Age ◽  
And Gender ◽  
Assistive Robotic

AbstractIn thiswork, a real-time system able to automatically recognize soft-biometric traits is introduced and used to improve the capability of a humanoid robot to interact with humans. In particular the proposed system is able to estimate gender and age of humans in images acquired from the embedded camera of the robot. This knowledge allows the robot to properly react with customized behaviors related to the gender/age of the interacting individuals. The system is able to handle multiple persons in the same acquired image, recognizing the age and gender of each person in the robot’s field of view. These features make the robot particularly suitable to be used in socially assistive applications.

scholarly journals Digital Vein Mapping Using Augmented Reality

2020 ◽  
Vol 13 (6) ◽  
pp. 512-521
Author(s):  
Mohamed Taha ◽  
◽  
Mohamed Ibrahim ◽  
Hala Zayed ◽  
◽  
...  
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Infrared Radiation ◽  
Low Cost ◽  
Economic Cost ◽  
Frame Rate ◽  
Infrared Light ◽  
Real Time Processing ◽  
And Gender ◽  
Time Systems

Vein detection is an important issue for the medical field. There are some commercial devices for detecting veins using infrared radiation. However, most of these commercial solutions are cost-prohibitive. Recently, veins detection has attracted much attention from research teams. The main focus is on developing real-time systems with low-cost hardware. Systems developed to reduce costs suffer from low frame rates. This, in turn, makes these systems not suitable for real-world applications. On the other hand, systems that use powerful processors to produce high frame rates suffer from high costs and a lack of mobility. In this paper, a real-time vein mapping prototype using augmented reality is proposed. The proposed prototype provides a compromised solution to produce high frame rates with a low-cost system. It consists of a USB camera attached to an Android smartphone used for real-time detection. Infrared radiation is employed to differentiate the veins using 20 Infrared Light Emitting Diodes (LEDs). The captured frames are processed to enhance vein detection using light computational algorithms to improve real-time processing and increase frame rate. Finally, the enhanced view of veins appears on the smartphone screen. Portability and economic cost are taken into consideration while developing the proposed prototype. The proposed prototype is tested with people of different ages and gender, as well as using mobile devices of different specifications. The results show a high vein detection rate and a high frame rate compared to other existing systems.

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

2022 ◽  
pp. 166-201
Author(s):  
Asha Gowda Karegowda ◽  
Devika G.
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Parameter Optimization ◽  
Heuristic Algorithms ◽  
Search Space ◽  
Optimization Method ◽  
Classification Problems ◽  
Local Optima ◽  
Consistent Solution ◽  
Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

An Improved Real-Time Recommendation for Microblogs Based on Topic

2019 ◽  
Vol 34 (02) ◽  
pp. 2059006
Author(s):  
Yikui Shi ◽  
Jiyan Liu ◽  
Lei Shi ◽  
Jianwen Zhao ◽  
Na Su
Keyword(s):  
Real Time ◽  
Information Overload ◽  
Rapid Development ◽  
Search Space ◽  
Decay Function ◽  
Recommendation Algorithm ◽  
User Similarity ◽  
User Clustering ◽  
Intermediate Variables ◽  
Over Time

With the rapid development of the Internet, people are confronted with information overload. Many recommendation methods are designed to solve this problem. The main contributions of recommendation methods proposed in this paper are as follows: (1) An improved collaborative filtering recommendation algorithm based on user clustering is proposed. Clustering is performed according to user similarity based on the user-item rating matrix. So the search space of recommendation algorithm is reduced. (2) Considering the factor that user’s interests may dynamically change over time, a time decay function is introduced. (3) A method of real-time recommendation based on topic for microblogs is designed to realize real-time recommendation effectively by preserving intermediate variables of user similarity. Experiments show that the proposed algorithms have been improved in terms of running time and accuracy.

scholarly journals Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users

2020 ◽  
Vol 10 (17) ◽  
pp. 5957 ◽  
Author(s):  
Roberto Garcia-Guzman ◽  
Yair A. Andrade-Ambriz ◽  
Mario-Alberto Ibarra-Manzano ◽  
Sergio Ledesma ◽  
Juan Carlos Gomez ◽  
...  
Keyword(s):  
Text Analysis ◽  
State Of The Art ◽  
Essential Feature ◽  
User Profile ◽  
Age And Gender ◽  
Category Information ◽  
Gender And Age ◽  
Twitter Users ◽  
And Gender ◽  
Number Of Publications

Category suggestions or recommendations for customers or users have become an essential feature for commerce or leisure websites. This is a growing topic that follows users’ activity in social networks generating a huge quantity of information about their interests, contacts, among many others. These data are usually collected to analyze people’s behavior, trends, and integrate a complete user profile. In this sense, we analyze a dataset collected from Pinterest to predict the gender and age by processing input images using a Convolutional Neural Network. Our method is based on the meaning of the image rather than the visual content. Additionally, we propose a heuristic-based approach for text analysis to predict users’ age and gender from Twitter. Both of the classifiers are based on text and images and they are compared with various similar approaches in the state of the art. Suggested categories are based on association rules conformed by the activity of thousands of users in order to estimate trends. Computer simulations showed that our approach can recommend interesting categories for a user analyzing his current interest and comparing this interest with similar users’ profiles or trends and, therefore, achieve an improved user profile. The proposed method is capable of predicting the user’s age with high accuracy, and at the same time, it is able to predict gender and category information from the user. The certainty that one or more suggested categories be interesting to people is higher for those users with a large number of publications.

scholarly journals A Novel Approach to Improve the Performance of Evolutionary Methods for Nonlinear Constrained Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Alireza Rowhanimanesh ◽  
Sohrab Efati
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Search Space ◽  
Global Optimum ◽  
Constrained Optimization Problems ◽  
Nonlinear Constrained Optimization ◽  
Evolutionary Methods ◽  
Space Reduction ◽  
Reduced Space

Evolutionary methods are well-known techniques for solving nonlinear constrained optimization problems. Due to the exploration power of evolution-based optimizers, population usually converges to a region around global optimum after several generations. Although this convergence can be efficiently used to reduce search space, in most of the existing optimization methods, search is still continued over original space and considerable time is wasted for searching ineffective regions. This paper proposes a simple and general approach based on search space reduction to improve the exploitation power of the existing evolutionary methods without adding any significant computational complexity. After a number of generations when enough exploration is performed, search space is reduced to a small subspace around the best individual, and then search is continued over this reduced space. If the space reduction parameters (red_gen and red_factor) are adjusted properly, reduced space will include global optimum. The proposed scheme can help the existing evolutionary methods to find better near-optimal solutions in a shorter time. To demonstrate the power of the new approach, it is applied to a set of benchmark constrained optimization problems and the results are compared with a previous work in the literature.

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